
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


lowercase    :   Tuple						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase    :   Optional[Any]						=			'\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n        ```\n'




@dataclass
class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	42




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:int	,  a						:PriorTransformer	,  a						:CLIPVisionModel	,  a						:CLIPImageProcessor	,  a						:HeunDiscreteScheduler	,  a						:ShapERenderer	,  )						->					List[str]:
								super().__init__()

								self.register_modules(
								    prior=a	,  image_encoder=a	,  image_processor=a	,  scheduler=a	,  renderer=a	,  )







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:List[str]	,  a						:int	,  a						:Dict	,  a						:Tuple	,  a						:List[Any]	,  a						:Optional[int]		)						->					List[Any]:
								if latents is None:
															__UpperCamelCase      :  List[Any]       =   randn_tensor(a	,  generator=a	,  device=a	,  dtype=a		)
								else:
															if latents.shape != shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
															__UpperCamelCase      :  Optional[Any]       =   latents.to(a		)

								__UpperCamelCase      :  Optional[int]       =   latents * scheduler.init_noise_sigma
								return latents







	def   _lowerCamelCase							(   self						:Optional[int]	,  a						:Union[str, Any]=0		)						->					List[str]:
								if is_accelerate_available():
															from accelerate import cpu_offload
								else:
															raise ImportError("Please install accelerate via `pip install accelerate`"		)

								__UpperCamelCase      :  Any       =   torch.device(f'cuda:{gpu_id}'		)

								__UpperCamelCase      :  Optional[int]       =   [self.image_encoder, self.prior]
								for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																						cpu_offload(a	,  a		)







	@property
	def   _lowerCamelCase							(   self						:str		)						->					str:
								if self.device != torch.device("meta"		) or not hasattr(self.image_encoder	,  "_hf_hook"		):
															return self.device
								for module in self.image_encoder.modules():
															if (
															    hasattr(a	,  "_hf_hook"		)
															    and hasattr(module._hf_hook	,  "execution_device"		)
															    and module._hf_hook.execution_device is not None
															):
																						return torch.device(module._hf_hook.execution_device		)
								return self.device







	def   _lowerCamelCase							(   self						:List[str]	,  a						:List[str]	,  a						:Dict	,  a						:Dict	,  a						:Optional[Any]	,  )						->					List[str]:
								if isinstance(a	,  a		) and isinstance(image[0]	,  torch.Tensor		):
															__UpperCamelCase      :  Optional[Any]       =   torch.cat(a	,  axis=0		) if image[0].ndim == 4 else torch.stack(a	,  axis=0		)

								if not isinstance(a	,  torch.Tensor		):
															__UpperCamelCase      :  List[Any]       =   self.image_processor(a	,  return_tensors="pt"		).pixel_values[0].unsqueeze(0		)

								__UpperCamelCase      :  str       =   image.to(dtype=self.image_encoder.dtype	,  device=a		)

								__UpperCamelCase      :  Tuple       =   self.image_encoder(a		)["last_hidden_state"]
								__UpperCamelCase      :  Union[str, Any]       =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

								__UpperCamelCase      :  List[str]       =   image_embeds.repeat_interleave(a	,  dim=0		)

								if do_classifier_free_guidance:
															__UpperCamelCase      :  List[str]       =   torch.zeros_like(a		)

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															__UpperCamelCase      :  Optional[int]       =   torch.cat([negative_image_embeds, image_embeds]		)

								return image_embeds







	@torch.no_grad()
	@replace_example_docstring(a		)
	def __call__(   self						:List[Any]	,  a						:Union[PIL.Image.Image, List[PIL.Image.Image]]	,  a						:int = 1	,  a						:int = 2_5	,  a						:Optional[Union[torch.Generator, List[torch.Generator]]] = None	,  a						:Optional[torch.FloatTensor] = None	,  a						:float = 4.0	,  a						:int = 6_4	,  a						:Optional[str] = "pil"	,  a						:bool = True	,  )						->					Dict:

								if isinstance(a	,  PIL.Image.Image		):
															__UpperCamelCase      :  Optional[int]       =   1
								elif isinstance(a	,  torch.Tensor		):
															__UpperCamelCase      :  Optional[Any]       =   image.shape[0]
								elif isinstance(a	,  a		) and isinstance(image[0]	,  (torch.Tensor, PIL.Image.Image)		):
															__UpperCamelCase      :  Union[str, Any]       =   len(a		)
								else:
															raise ValueError(
															    f'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(a		)}'		)

								__UpperCamelCase      :  Dict       =   self._execution_device

								__UpperCamelCase      :  Optional[Any]       =   batch_size * num_images_per_prompt

								__UpperCamelCase      :  Dict       =   guidance_scale > 1.0
								__UpperCamelCase      :  str       =   self._encode_image(a	,  a	,  a	,  a		)

								# prior

								self.scheduler.set_timesteps(a	,  device=a		)
								__UpperCamelCase      :  int       =   self.scheduler.timesteps

								__UpperCamelCase      :  Optional[Any]       =   self.prior.config.num_embeddings
								__UpperCamelCase      :  int       =   self.prior.config.embedding_dim

								__UpperCamelCase      :  Union[str, Any]       =   self.prepare_latents(
								    (batch_size, num_embeddings * embedding_dim)	,  image_embeds.dtype	,  a	,  a	,  a	,  self.scheduler	,  )

								# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
								__UpperCamelCase      :  int       =   latents.reshape(latents.shape[0]	,  a	,  a		)

								for i, t in enumerate(self.progress_bar(a		)		):
															# expand the latents if we are doing classifier free guidance
															__UpperCamelCase      :  Union[str, Any]       =   torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
															__UpperCamelCase      :  int       =   self.scheduler.scale_model_input(a	,  a		)

															__UpperCamelCase      :  Optional[Any]       =   self.prior(
															    a	,  timestep=a	,  proj_embedding=a	,  ).predicted_image_embedding

															# remove the variance
															__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   noise_pred.split(
															    scaled_model_input.shape[2]	,  dim=2		)  # batch_size, num_embeddings, embedding_dim

															if do_classifier_free_guidance is not None:
																						__UpperCamelCase  ,		__UpperCamelCase      :  Optional[Any]       =   noise_pred.chunk(2		)
																						__UpperCamelCase      :  Optional[int]       =   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

															__UpperCamelCase      :  Tuple       =   self.scheduler.step(
															    a	,  timestep=a	,  sample=a	,  ).prev_sample

								if output_type == "latent":
															return ShapEPipelineOutput(images=a		)

								__UpperCamelCase      :  int       =   []
								for i, latent in enumerate(a		):
															print()
															__UpperCamelCase      :  Union[str, Any]       =   self.renderer.decode(
															    latent[None, :]	,  a	,  size=a	,  ray_batch_size=4_0_9_6	,  n_coarse_samples=6_4	,  n_fine_samples=1_2_8	,  )

															images.append(a		)

								__UpperCamelCase      :  Any       =   torch.stack(a		)

								if output_type not in ["np", "pil"]:
															raise ValueError(f'Only the output types `pil` and `np` are supported not output_type={output_type}'		)

								__UpperCamelCase      :  int       =   images.cpu().numpy()

								if output_type == "pil":
															__UpperCamelCase      :  Tuple       =   [self.numpy_to_pil(a		) for image in images]

								# Offload last model to CPU
								if hasattr(self	,  "final_offload_hook"		) and self.final_offload_hook is not None:
															self.final_offload_hook.offload()

								if not return_dict:
															return (images,)

								return ShapEPipelineOutput(images=a		)

import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
		import torch


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowercase    :   Any						=			logging.get_logger(__name__)
lowercase    :   Union[str, Any]						=			{'vocab_file': 'spiece.model'}

lowercase    :   Tuple						=			{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

lowercase    :   List[str]						=			{
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	VOCAB_FILES_NAMES
	_A								=	PRETRAINED_VOCAB_FILES_MAP
	_A								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=	['input_ids', 'attention_mask']







	def __init__(   self						:int	,  a						:List[Any]	,  a						:int=False	,  a						:int=False	,  a						:List[str]=False	,  a						:Optional[Any]=None	,  a						:Tuple=None	,  a						:Optional[Any]=None	,  a						:Any=None	,  a						:Optional[Dict[str, Any]] = None	,  **a						:Dict	,  )						->					None:
								__UpperCamelCase      :  List[str]       =   {} if sp_model_kwargs is None else sp_model_kwargs

								__UpperCamelCase      :  Any       =   kwargs.get("name_or_path"		)
								if name_or_path is None:
															logger.warning(
															    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
															    " you are testing the model, this can safely be ignored"		)
															__UpperCamelCase      :  Any       =   "None"

								# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
								__UpperCamelCase      :  Union[str, Any]       =   "<|endoftext|>" if eos_token is None else eos_token
								__UpperCamelCase      :  int       =   "<unk>" if unk_token is None else unk_token
								if "gpt-sw3-7b" in name_or_path:
															__UpperCamelCase      :  str       =   unk_token if pad_token is None else pad_token
															__UpperCamelCase      :  Dict       =   eos_token if bos_token is None else bos_token
								else:
															__UpperCamelCase      :  Optional[int]       =   "<pad>" if pad_token is None else pad_token
															__UpperCamelCase      :  Any       =   "<s>" if bos_token is None else bos_token

								super().__init__(
								    do_lower_case=a	,  remove_space=a	,  keep_accents=a	,  bos_token=a	,  eos_token=a	,  unk_token=a	,  pad_token=a	,  sp_model_kwargs=self.sp_model_kwargs	,  **a	,  )

								__UpperCamelCase      :  List[str]       =   do_lower_case
								__UpperCamelCase      :  List[str]       =   remove_space
								__UpperCamelCase      :  int       =   keep_accents
								__UpperCamelCase      :  List[Any]       =   vocab_file

								__UpperCamelCase      :  Optional[Any]       =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(a		)

								# Used for whitespace normalization in input texts
								# fmt : off
								__UpperCamelCase      :  Dict       =   {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
								# fmt : on

								# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
								__UpperCamelCase      :  int       =   re.compile(
								    f'[{"".join(map(a	,  list(range(0	,  9		)		) + list(range(1_1	,  3_2		)		) + list(range(1_2_7	,  1_6_0		)		) + [1_6_0, 1_7_3, 8_2_0_3]		)		)}]'		)







	def __getstate__(   self						:Tuple		)						->					Union[str, Any]:
								__UpperCamelCase      :  int       =   self.__dict__.copy()
								__UpperCamelCase      :  Optional[Any]       =   None
								return state







	def __setstate__(   self						:Union[str, Any]	,  a						:Optional[int]		)						->					Tuple:
								__UpperCamelCase      :  int       =   d

								# for backward compatibility
								if not hasattr(self	,  "sp_model_kwargs"		):
															__UpperCamelCase      :  Optional[int]       =   {}

								__UpperCamelCase      :  Dict       =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	@property
	# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
	def   _lowerCamelCase							(   self						:int		)						->					int:
								return len(self.sp_model		)







	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					str:
								__UpperCamelCase      :  int       =   self.non_printing_characters_re.sub(""	,  a		)

								# Normalize whitespaces
								__UpperCamelCase      :  Any       =   "".join([char if char not in self.whitespaces else " " for char in text]		)

								# NFC Unicode normalization
								__UpperCamelCase      :  str       =   unicodedata.normalize("NFC"	,  a		)
								return text







	def   _lowerCamelCase							(   self						:int	,  a						:str	,  **a						:str		)						->					List[str]:
								__UpperCamelCase      :  Any       =   self.preprocess_text(a		)
								return self.sp_model.encode(a	,  out_type=a		)







	def   _lowerCamelCase							(   self						:int	,  a						:str		)						->					int:
								return self.sp_model.PieceToId(a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:int		)						->					str:
								return self.sp_model.IdToPiece(a		)







	@staticmethod
	def   _lowerCamelCase							(   a						:str		)						->					str:
								return out_string







	def   _lowerCamelCase							(   self						:Optional[int]	,  a						:List[str]		)						->					str:
								__UpperCamelCase      :  List[Any]       =   []
								__UpperCamelCase      :  Tuple       =   ""
								__UpperCamelCase      :  Optional[Any]       =   False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																						if not prev_is_special:
																													out_string += " "

																						out_string += self.sp_model.decode(a		) + token
																						__UpperCamelCase      :  int       =   True
																						__UpperCamelCase      :  Any       =   []
															else:
																						current_sub_tokens.append(a		)
																						__UpperCamelCase      :  Dict       =   False
								out_string += self.sp_model.decode(a		)

								return out_string







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Dict[str, int]:
								__UpperCamelCase      :  Tuple       =   {self.convert_ids_to_tokens(a		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def   _lowerCamelCase							(   self						:Any	,  a						:str	,  a						:Optional[str] = None		)						->					Tuple[str]:
								if not os.path.isdir(a		):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
															return
								__UpperCamelCase      :  int       =   os.path.join(
								    a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(a		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file	,  a		)
								elif not os.path.isfile(self.vocab_file		):
															with open(a	,  "wb"		) as fi:
																						__UpperCamelCase      :  Dict       =   self.sp_model.serialized_model_proto()
																						fi.write(a		)

								return (out_vocab_file,)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, List[str]]	,  a						:Union[str, bool] = False		)						->					Union[List[int], List[List[int]], "torch.Tensor"]:

								if isinstance(a	,  a		):
															__UpperCamelCase      :  Optional[Any]       =   self.preprocess_text(a		)
															__UpperCamelCase      :  List[str]       =   self.sp_model.encode(a		)
								else:
															__UpperCamelCase      :  List[str]       =   [self.preprocess_text(a		) for t in text]
															__UpperCamelCase      :  str       =   self.sp_model.encode(a		)

								if return_tensors is True or return_tensors == "pt":
															__UpperCamelCase      :  Any       =   torch.tensor(a		)

								return token_ids







	def   _lowerCamelCase							(   self						:Any	,  a						:Union[int, List[int]]		)						->					str:
								return self.sp_model.decode(a		)







	def   _lowerCamelCase							(   self						:Dict	,  a						:"Conversation"		)						->					List[int]:
								__UpperCamelCase      :  Dict       =   [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
								__UpperCamelCase      :  int       =   (
								    f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(a		) + f'{self.bos_token}Bot:'
								)
								return self.encode(text=a		)